One the one hand, complex technologies o↵er substantial economic benefits, and on the other, they are di cult to invent and to imitate, and they refuse a fast dissemination. This two-sidedness motivates the idea that regions' competitive advantages and, in consequence, their economic growth, originate in their ability to produce and utilize complex technologies. However, the relationship between technological complexity and regional economic growth has rarely been empirically investigated. Here, we address this pressing issue by assessing the complexity of technological activities in 159 European NUTS 2 regions and relating it to their economic growth from 2000 to 2014. Our empirical results suggest that technological complexity is an important predictor of regional economic growth. A 10% increase in complexity is associated with a 0.45% GDP per capita growth. By showing that technological complexity is important for regional economic growth, our results fuel current policy debates about optimal regional policies such as the Smart Specialization strategy.

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Previous research shows ample evidence that regional diversification is strongly path dependent, as regions are more likely to diversify into related than unrelated activities. In this paper, we ask whether contemporary innovation policy in form of R&D subsidies intervenes in the process of regional diversification. We focus on R&D subsidies and assess whether they cement existing path dependent developments, or whether they help in breaking these by facilitating unrelated diversification. To investigate the role of R&D policy in the process of regional technological diversification, we link information on R&D subsidies with patent data and analyze the diversification of 141 German labor-market regions into new technology classes between 1991 and 2010. Our findings suggest that R&D subsidies positively influence regional technological diversification. In addition, we find significant differences between types of subsidy. Subsidized joint R&D projects have a larger effect on the entry probabilities of technologies than subsidized R&D projects conducted by single organizations. To some extent, collaborative R&D can even compensate for missing relatedness by facilitating diversification into unrelated technologies.

Psychologists have become increasingly interested in the geographical organization of psychological phenomena. Such studies typically seek to identify geographical variation in psychological characteristics and examine the causes and consequences of that variation. Geo-psychological research offers unique advantages, such as a wide variety of easily obtainable behavioral outcomes. However, studies at the geographically aggregate level also come with unique challenges that require psychologists to work with unfamiliar data formats, sources, measures, and statistical problems. The present article aims to present psychologists with a methodological roadmap that equips them with basic analytical techniques for geographical analysis. Across five sections, we provide a step-by-step tutorial and walk readers through a full geo-psychological research project. We provide guidance for (a) choosing an appropriate geographical level and aggregating individual data, (b) spatializing data and mapping geographical distributions, (c) creating and managing spatial weights matrices, (d) assessing geographical clustering and identifying distributional patterns, and (e) regressing spatial data using spatial regression models. Throughout the tutorial, we alternate between explanatory sections that feature in-depth background information and hands-on sections that use real data to demonstrate the practical implementation of each step in R. The full R code and all data used in this demonstration are available from the OSF project page accompanying this article.

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